Climate Stations Research Articles

MERRA-2 reanalysis-aided sea fog detection based on CALIOP observation over North Pacific

Sea fog can lead to a dangerous meteorological hazard over oceans and coastal areas. Cloud-aerosol Lidar with Orthogonal Polarization (CALIOP) is an effective tool to detect sea fog, however it is ineffective for dense fog detection because the lidar signal has been fully attenuated before reaching the layer base. To address this issue by improving dense fog detection, a reanalysis-aided sea fog detection method using CALIOP lidar measurements and MERRA-2 reanalysis dataset is proposed taking the Northwestern Pacific (NP) as study area. In this method, CALIOP lidar measurements are used to locate the ceiling and base of the hydrometeor layers, and extract the candidate fog layers in the Marine Atmospheric Boundary Layer (MABL). Reanalysis dataset are used to identify real fog from the candidate fog layers based on the near-surface meteorological difference under different weather conditions (sea fog, stratus and stratus precipitation). Method validation using in-situ weather records from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) and five climate stations, shows the detection accuracy and false alarm rate of 89.4% and 2.0%, respectively. Most importantly, about 87.4% of dense fogs are successfully discriminated with the aid of reanalysis data. Model application to CALIOP data from 2007 to 2019 shows pronounced spatial and seasonal characteristics of sea fog distribution over NP, with the highest frequency (>40%) found over the Kuril Islands, Sea of Okhotsk and Bering Sea from June to August. The example demonstrates that by using time series CALIOP observations and the proposed method, one may expect spatio-temporally complete sea fog information, which is important especially for remote regions with scarce in-situ measurements (e.g. polar regions). Our research proposes and proves a novel idea of sea fog detection from space by using satellite lidar and reanalysis data, which may benefit the sea fog forecasting and dynamics analysis, by providing unprecedented large-scale high-quality observations, especially over dense fog conditions.

Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model.

The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation water use efficiency, it is necessary to predict the water requirements for crops in advance. Reference evapotranspiration (ETo) is a hypothetical standard reference crop evapotranspiration, many types of artificial intelligence models have been applied to predict ETo; However, there are still few in the literature regarding the application of hybrid models for deep learning model parameters optimization. This paper proposes two hybrid models based on particle swarm optimization (PSO) and long-short-term memory (LSTM) neural network, used to predict ETo at the four climate stations, Shaanxi province, China. These two hybrid models were trained using 40 years of historical data, and the PSO was used to optimize the hyperparameters in the LSTM network. We applied the optimized model to predict the daily ETo in 2019 under different datasets, the result showed that the optimized model has good prediction accuracy. The optimized hybrid models can help farmers and irrigation planners to make plan earlier and precisely, and can provide valuable information to improve tasks such as irrigation planning.

Climate Change Data: Use of an Autoregressive (AR) Model in Presence of Change Points under a Bayesian Approach

In this study, we introduce a statistical model applied to climate change data consisting of an autoregressive times series (AR) model which represents a type of random process. A Bayesian approach using MCMC (Markov Chain Monte Carlo) methods is considered to get the inferences of interest. The main goal of the study is to have a model to get good predictions for mean temperature and also good to identify the time of possible change-points that might be present in the time series which could indicate the possible beginning of a change in climate. Applications of the proposed model are considered using annual average temperatures in some locations obtained over a period of time ranging from the end of 1800’s to a popular Bayesian discrimination criterion using MCMC methods.In addition to a good fit of the proposed model for the data, the model also was used to detect the times of climate changes in the different climate stations using CUSUM methodology.

Trend analysis of hydro-climate variables in the Jemma sub-basin of Upper Blue Nile (Abbay) Basin, Ethiopia

The change in climate elements such as rainfall and temperature are determinant factors of hydrological components (e.g., streamflow, water yield, evapotranspiration). Thus, understanding the trends of hydro-climate variables are imperative for planning water resources management measures. This study examines the trends of precipitation and temperature (1980–2014) as well as streamflow (1990–2008) changes in the Jemma sub-basin of the Upper Blue Nile (Abbay) Basin. A homogeneity test was performed for pre-processing data to inspect the statistical nature of data homogeneity viz., Pettitt’s, SNHT, Buishand range test, and Von Neumann test (VNT). The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Sen’s slope estimator, and Innovative trend analysis (ITA) were applied to compute the existence of trend and the magnitude of change at an annual timescale. In a nutshell, the study portrays a change in the trends of hydro-climate variables when using different statistical tests. The result showed that the mean annual temperature in many stations depicted upward trends. There is a significant increasing trend (p < 0.05) by 0.029 °C per year in the mean annual temperature of all climatic stations. Based on Sen’s slope estimator, the annual precipitation and streamflow were increased by 1.781 mm/year and 0.085 m3/s, respectively. But no significant trends were detected in precipitation and streamflow when using Sen’s slope estimator test. The results of this study are worthwhile for evaluating the trends of hydro-climatic variables in other areas of Ethiopia in particular and elsewhere in the world in general, which are fundamental for planning water resource management measures.

Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data

This study investigates the feasibility of a relevance vector machine tuned with improved Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using limited climatic input data (e.g. temperature). The accuracy of the RVM-IMRFO was evaluated by comparing it with RVM tuned by gray wolf optimization, RVM tuned with a whale optimization algorithm, and RVM tuned with Manta Ray foraging optimization concerning root mean square errors (RMSE), mean absolute errors (MAE), determination coefficient (R2) and Nash-Sutcliffe Efficiency (NSE) and new graphical inspection methods. The models were assessed using data acquired from two stations in China and data were divided into three equal parts. The models were tested using each data set. The application outcomes revealed that the proposed algorithm considerably improved the accuracy of a single RVM in monthly pan evaporation prediction by an average improvement in RMSE, MAE, R2, and NSE as 27.65%, 27.53%, 8.40% and 8.63%, respectively. It is also found that the proposed algorithm showed significant dominance over others models with respect to improvement in overall mean values of RMSE, MAE, R2, and NSE statistics from 34.7–38.2 to 18.2–19.5, 36.2–36.4 to 19.1–18.5, 12.5–13.8 to 3.6–3.7, and 12.4–14.6 to 3.6–3.9%, for both climatic stations, respectively. Importing extraterrestrial radiation and periodicity component (month number of the data) into the model inputs improved the prediction accuracy of the implemented models. The outcomes revealed that the RVM-IMRFO performed superior to the other methods in predicting monthly pan evaporation using only temperature data which is essential, especially in developing countries where other climatic data are missing or unavailable. The RVM model was also compared with standard multi-layer perceptron neural networks (MLPNN) and found that the first acts better than the latter in monthly pan evaporation prediction.

Comparison of deterministic wind speed generation models for wind erosion modeling

The diurnal patterns of hourly wind speed data are fundamentally important in precise wind erosion modeling. Several deterministic wind speed simulation models, including the Guo (1/π), Guo (1/2), wind generator (WINDGEN), Ephrath, Debele, and Goudriaan models, have been developed to quantitatively generate hourly wind speed data. In this study, the performances of these popular deterministic wind speed generation models were investigated using observed hourly wind speed data obtained from 191 climate stations in arid and semi-arid areas of China. The performances of the Guo (1/π), Guo (1/2) and WINDGEN models were the best for hourly wind speed generation. Linear regression analysis revealed that all models tended to underestimate wind speeds. The patterns of diurnal variation and the amplitude of the measured hourly wind speed data generally governed the model performance. The performances of the Guo (1/π), WINDGEN, Guo (1/2), Goudriaan, Debele, and Ephrath models successively decreased for the daily average erosive wind power density (AWPD) estimation. To improve the Guo (1/π) and Guo (1/2) models for AWPD evaluation, the calibrated parameters (π and 2) of the second term in the Guo (1/π) and Guo (1/2) models showed different values for each station. The accuracy of the AWPD obtained by the Guo (1/π) and Guo (1/2) models with the calibrated parameters were significantly improved, which could further influence the accuracy of wind erosion modeling. More studies are required to investigate the relationship between the calibrated parameters in the Guo (1/π) and Guo (1/2) models and geographic conditions linked with wind erosion.

Use of Segmented Linear Regression Under a Bayesian Approach to Detect Climate Change in Different Regions of the World

In this work we study the behavior of some climate data (annual temperature and precipitation averages) obtained from climate stations in eleven countries in different regions of the world. One of the goals of the study is to determine whether the climate variables have change-points that could indicate the possible beginning of a change in climate. Another goal is to analyze the possible changes detected by the change-points in terms of the linear trends of the climate variables under investigation. Based on the information provided, differences between different regions in terms of the locations of the change-points and the changes they produce may also be inferred. The data sets used in the study consist of the annual averages of the twelve monthly temperature averages and the annual averages of the total rain precipitation observed in each one of the twelve months of the year obtained over a period of time from the end of the 19th century to the end of the 20th century. Segmented linear regression models are used to study the existence of possible changes in the behavior of climatic variables, as well as the types of changes produced.

Analysis of rainfall and temperature variability for agricultural water management in the upper Genale river basin, Ethiopia

A better understanding of climate-induced changes and support for adaptation strategies requires knowledged of the spatiotemporal dynamics of climatic variables. This study aims to assess long-term changes in annual and seasonal rainfall and temperature for agricultural water management in the upper Genale river basin, Ethiopia. To achieve this objective, long-term climate data from 13 stations between 1975 and 2018, as well as maximum and minimum temperatures for six stations were collected from the Ethiopian National Meteorological Service Agency (NMA). The characteristics of rainfall (onset, cessation, length of growing season, and crop water requirement) were assessed using the Coefficient of Variation (CV), Standard Rainfall Anomaly (SRA), and Precipitation Concentration Index (PCI). Additionally, the Mann–Kendall test and Sen's slope estimator were used to assess the trend and magnitude of changes in rainfall and temperature. The annual, winter, spring, summer and autumn CVs were 20%, 89%, 30%, 45%, and 32%, respectively. The standardized anomalies of annual and seasonal rainfall for the climate stations indicated that the basin had a drier season than a wet season. The mean length of the growing season in Belg and Kiremt ranged from 43 to 79 days and 38 to 170 days, respectively. On an annual, summer, and autumn season basis, most rainfall stations showed a non-significant trend. In contrast, in the spring season, rainfall showed a decreasing but statistically insignificant trend in all stations except Bensadaye, Bore, Telamokentise, and Yirba Muda stations. According to the analysis of the crop water requirements in the studied basin, supplemental irrigation is essential to reducing yield reduction and crop failure during spring and autumn. In these seasons, maize and sorghum varieties required supplementary irrigation up to 202 mm and 252 mm, respectively. Annual and seasonal maximum and minimum temperatures showed an increasing trend at all the stations except Hagre Selam station. Generally, there was a spatially and temporally variable trend in rainfall while the temperature trend was increasing irrespective of the temporal and spatial factors. Thus, this study contributes to a better understanding of climate change in the basin and these findings can be used in planning for adaptation measures against a changing climate.

A GIS-based approach for estimating fallow-season cropland soil erosion based on rainfall erosivity

ABSTRACT Rainfall erosivity describes the capability of rainfall to cause soil erosion from a surface during a storm. Currently, the average long-term annual and monthly rainfall erosivity for a location may be obtained from isoerodent maps using a sample of climate stations across the United States (US). However, at the state-scale these measures are sporadic, using rainfall data from an increasingly outdated period, which may not reflect possible changing rainfall erosivity. Focusing on the state of Kentucky, US, we developed rainfall erosivity grids from the state Mesonet system to determine if erosivity has changed spatially and temporally between the original erosivity datasets from the latter twentieth-century and 2011–2020. We further used the Mesonet-derived dataset to develop a GIS-based model to estimate potential soil erosion for croplands during the fallow-season when soil is most prone to erosive storms. Results indicated that monthly erosivity increased across the state between the two periods. Larger increases in erosivity during the latter part of the fallow-season resulted in higher estimated soil erosion from croplands, particularly to the west. These findings may provide guidance in identifying and targeting croplands at greatest potential risk of soil erosion during the fallow-season for additional monitoring, mitigation and management practices.

Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting

Precise Air temperature modeling is crucial for a sustainable environment. In this study, a novel binary optimized machine learning model, the random vector functional link (RVFL) with the integration of Moth Flame Optimization Algorithm (MFO) and Water Cycle Optimization Algorithm (WCA) is examined to estimate the monthly and daily temperature time series of Rajshahi Climatic station in Bangladesh. Various combinations of temperature and precipitation were used to predict the temperature time series. The prediction ability of the novel binary optimized machine learning model (RVFL-WCAMFO) is compared with the single optimized machine learning models (RVFL-WCA and RVFL-MFO) and the standalone machine learning model (RVFL). Root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2) statistical indexes were utilized to access the prediction ability of the selected models. The proposed binary optimized machine learning model (RVFL-WCAMFO) outperformed the other single optimized and standalone machine learning models in prediction of air temperature time series on both scales, i.e., daily and monthly scale. Cross-validation technique was applied to determine the best testing dataset and it was found that the M3 dataset provided more accurate results for the monthly scale, whereas the M1 dataset outperformed the other two datasets on the daily scale. On the monthly scale, periodicity input was also added to see the effect on prediction accuracy. It was found that periodicity input improved the prediction accuracy of the models. It was also found that precipitation-based inputs did not provided very accurate results in comparison to temperature-based inputs. The outcomes of the study recommend the use of RVFL-WCAMFO in air temperature modeling.

Trends in Rainfall and Temperature Extremes in Ethiopia: Station and Agro-Ecological Zone Levels of Analysis

Climate extreme events have been observed more frequently since the 1970s throughout Ethiopia, which adversely affects the socio-economic development of the country, as its economy depends on agriculture, which, in turn, relies heavily on annual and seasonal rainfall. Climate extremes studies conducted in Ethiopia are mainly limited to a specific location or watershed, making it difficult to have insights at the national level. The present study thus aims to examine the observed climate extreme events in Ethiopia at both station and agro-ecological zone (AEZ) levels. Daily rainfall and temperature data for 47 and 37 stations, respectively (1986 up to 2020), were obtained from the National Meteorology Agency (NMA). The Modified Mann–Kendall (MMK) trend test and the Theil–Sen slope estimator were employed to estimate the trends in rainfall and temperature extremes. This study examines trends of 13 temperature and 10 rainfall extreme indices using RClimDex in R software. The results revealed that most of the extreme rainfall indices showed a positive trend in the majority of the climate stations. For example, an increase in consecutive dry days (CDD), very heavy rainfall days (R20), number of heavy rainfall days (R10) and consecutive wet days (CWD) were exhibited in most climate stations. In relation to AEZs, the greater number of extreme rainfall indices illustrated an upward trend in cool and sub-humid, cool and humid, and cool and moist AEZs, a declining trend in hot arid AEZ, and equal proportions of increasing and decreasing trends in warm semi-arid AEZs. Concerning extreme temperature indices, the result indicated an increasing trend of warm temperature extreme indices and a downward trend of cold temperature extreme indices in most of the climate stations, indicating the overall warming and dryness trends in the country. With reference to AEZs, an overall warming was exhibited in all AEZs, except in the hot arid AEZ. The observed trends in the rainfall and temperature extremes will have tremendous direct and indirect impacts on agriculture, water resources, health, and other sectors in the country. Therefore, the findings suggest the need for identifying and developing climate change adaptation strategies to minimize the ill effects of these extreme climate events on the social, economic, and developmental sectors.

Evaluation of the near-surface wind field over the Adriatic region: local wind characteristics in the convection-permitting model ensemble

We present the first evaluation of the wind field from the ensemble of kilometer-scale simulations from the CORDEX-Flagship Pilot Study on convection, with focus on the Adriatic region. Kilometer-scale climate models, also known as convection-permitting models (CPMs), produce a good representation of small-scale topographic features and consequently a more detailed depiction of dynamical and thermal circulations. These enable a reliable view of climate characteristics of the wind field, especially in coastal regions and over complex terrain, such as the Adriatic region. We investigate the (potential) added value introduced by CPMs compared to classical “cumulus-parametrized” regional climate models (RCMs), reanalysis and station observations. For this purpose, wind components at 10 m level are used at 3-hourly frequency. All simulations cover a 10-year period, extending from 2000 to 2009. In terms of the standard statistical parameters such as correlation coefficient and temporal standard deviation, CPMs are very dependent on their parent RCM performance. However, the orographic forcing emphasizes the potential added value and CPMs contain some fine spatial scale variability (i.e., stronger extremes by 25% and more accurate wind direction) that is absent in coarser RCMs and reanalysis. The potential added value is higher in the cold season compared to the warm season due to the proportion of severe wind events. CPMs reproduce well the typical wind regimes along the Adriatic coast, namely Bora and Sirocco. The benefit of using CPMs is especially pronounced in simulating Bora maximum wind speeds in northern Adriatic and Sirocco frequencies in southern Adriatic. Based on our overall analysis, we conclude that CPMs provide added value compared to coarser models, especially in the complex coastal terrain.

Initial results of the meteorological data from the first 325 sols of the Tianwen-1 mission

As the Zhurong rover landed on the surface of Mars in 2021, it began a months-long collection of Mars data. Equipped with highly sensitive sensors, Zhurong is capable of being a meteorological station at the surface of Mars. The Mars Climate Station, one of the onboard sensors with high sensitivity, helps the Tianwen-1 lander to collect meteorological data at the Martian surface, via which the air temperature, atmospheric pressure, wind speed and direction are measured. In this paper, we present results of surface pressure, air temperature and wind data from the Mars Climate Station at Zhurong’s landing site. The data is collected in 176 solar days out of the entire rover’s mission time, 325 solar days. We use a trigonometric function to fit the relationship between the solar longitude (Ls) and the pressure, after which we compare the results with those of Viking I. Our analysis of the temperature shows that seasonal evolution is similar to the patterns concluded in previous Mars missions at different landing sites. We discover that wind speed appears the maximum in early summer near Zhurong’s landing site, and analyze the occurrence of dust storms by combining the data of wind and temperature. Our results provide some evidence of the seasonal changes in meteorological pattern at Tianwen-1’s landing site, south of Utopia Planitia. With the mission ongoing further, more results are expected in the future.

Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed

In the mountainous Western U.S., a considerable portion of water supply originates as snowmelt passing through karst watersheds. Accurately simulating streamflow in snow-dominated, karst basins is important for water resources management. However, this has been challenging due to high spatiotemporal variability of meteorological and hydrogeological processes in these watersheds and scarcity of climate stations. To overcome these challenges, a physically based snow model is used to simulate snow processes at 100 m resolution, and the calculated snowmelt and potential evapotranspiration rates are fed into a deep learning model to simulate streamflow. The snow model was driven by meteorological variables from a regional scale Weather Research and Forecasting (WRF) model or from the North American Land Data Assimilation System (NLDAS-2). The two datasets were used both at the original resolution and downscaled to 100 m resolution based on orographic adjustments, leading to four sets of forcings. Snow model simulation results from the four sets of forcings showed large differences in simulated snow water equivalent (SWE) and snowmelt rate and timing. However, the differences were damped in simulated streamflow, as the deep learning model is partially immune to input bias and picked up different streamflow responses to snowmelt and rainfall when trained using snow model results. While the meteorological datasets considered yielded close streamflow simulation accuracy, averaging simulated streamflow from the four sets of forcings consistently achieved better performance, suggesting the value of including multiple meteorological datasets for modeling streamflow in mountainous watersheds.

Improved Landsat-based snow cover mapping accuracy using a spatiotemporal NDSI and generalized linear mixed model

Snow cover extent and distribution over the years have a significant impact on hydrological, terrestrial, and climatologic processes. Snow cover mapping accuracy using remote sensing data is then particularly important. This study analyses Landsat-8 NDSI snow cover datasets over time and space using different NDSI-based approach. The objectives are (i) to investigate the relation snow-NDSI with different environmental variables, (ii) to evaluate the accuracy of the common NDSI threshold of 0.4 against in-situ snow depth measurement and (iii) to develop a method that optimises snow cover mapping accuracy and minimises snow cover detection errors of omission and commission. Landsat-8 snow cover datasets were compared to ground snow depth measurements of climate stations over Switzerland for the period 2014–2020. It was found that there is a consistent relationship between NDSI values and land cover type, elevation, seasons, and snow depth measurements. The global NDSI threshold of 0.4 may not be always optimal for the Swiss territory and tends to underestimate the snow cover extent. Best NDSI thresholds vary spatially and are generally lower than 0.4 for the three snow depth threshold tested. We therefore propose a new spatiotemporal NDSI method to maximize snow cover mapping accuracy by using a generalized linear mixed model (GLMM). This model uses three environmental variables (i.e., elevation, land cover type and seasons) and raw NDSI values and improves snow cover mapping accuracy by 24% compared to the fixed threshold of 0.4. By using this method omissions errors decrease considerably while keeping a very low value of commission errors. This method will then be integrated in the Snow Observation from Space (SOfS) algorithm used for snow detection in Switzerland.

Design and implementation of payload system of the Zhurong Mars rover

<p indent="0mm">Using the scientific payload configured on the Mars rover to explore the Mars surface can conduct high-resolution <italic>in situ</italic> surveys in a critical area of Mars. The scientific payloads configured on the rover include Navigation and Terrain Camera (NaTeCam), Multispectral Camera (MSCam), Mars Rover Penetrating Radar (RoPeR), Mars Surface Composition Detector (MarSCoDe), Mars Rover Magnetometer (RoMAG) and Mars Climate Station (MCS). The payloads are controlled and managed by the integrated payload controller. In view of the demand for autonomous control of multiple payloads for collaborative scientific exploration under the resource shortage condition, the centralized payloads control method was adopted, and a novel integrated hardware architecture of payloads electronics, and payloads data management was established. An efficient autonomous payloads explore mode based on the working mode program was innovatively designed, and several health management measures were taken to perform fault detection, isolation and recovery (FDIR). The Zhurong Mars rover has successfully completed the scheduled exploration mission. All payloads work normally, and the exploration data are valid. This paper introduces the design and implementation of the payload system of the Mars rover.

Investigating the roles of different extracted parameters from satellite images in improving the accuracy of daily reference evapotranspiration estimation

Agricultural water management, crop modeling, and irrigation scheduling are all dependent on the accurate estimation of reference evapotranspiration (ET0). A satellite image can also compensate for the lack of reliable weather information. So, in this study, stochastic gradient descent (SGD) has been implemented for optimizing multilayer perceptron (MLP) and developing SGD-MLP to estimate daily ET0 in Tabriz (semi-arid climate) and Babolsar (humid climate) stations, Iran, using extracted data from satellite images. The estimated ET0 values were compared to the determined ET0 based on the FAO-Penman–Monteith equation. Based on satellite image data collected from 2003 to 2021, the database was constructed. During the development of the abovementioned models, data from 2003 to 2016 (70%) were used for training purposes, and residual data (30%) were used for testing purposes. Additionally, the input variables, including land surface temperature (LST) day and night, normalized difference vegetation index (NDVI), leaf area index (LAI), and a fraction of photosynthetically active radiation (FPAR) from MODIS sensor, were utilized to estimate the daily ET0. Thus, there are three studied models; first is based on the LST, second on the vegetation indices, and third on the combination of the LST and the vegetation indices. Additionally, four performance indexes, including the coefficient of determination (R2), the root-mean-square error (RMSE), Willmott’s index of agreement (WI), and Nash–Sutcliffe efficiency, were utilized in order to measure the implemented model’s accuracy. According to the obtained results, the SGD-MLP-3 with input parameters of LSTday&night, LSTmean, LAI, NDVI, and FPAR gave the most accurate results with RMSE and WI values of as 0.417 mm/day, 0.973, for Tabriz and 0.754 mm/day, 0.922 for Babolsar stations, respectively. Conclusively, LST of daytime, nighttime, and average may be suggested as the most influential parameter for ET0 estimation.

PROSES PENGOLAHAN KOPI ROBUSTA POROT TEMANGGUNG UNTUK MENGATASI KENDALA CUACA

It is important for the global coffee industry to make robusta coffee safely and on time. In this study, we look at how well a simple simulation-based method works and try to guess how much robusta coffee will grow in the Tuanggung area. The main process is the growth and development of coffee, which simulates its response to climate variations and potential water requirements during the foggy growing process. Such weather conditions make Robusta coffee unique in taste. This model was made and tested for people in Indonesia, especially in West Java, which has many coffee farms. The model building is very important for long-term coffee productivity studies in areas where the climate changes drastically and does not have long-term climate stations. This simple process-based model from research on community service (PkM) can be used as a starting point for making robusta coffee with a season that works with a weather forecasting system. This process will help farmers and businesses better prepare and manage their supply chains. For extreme weather changes and increased profitability.

PMEL Ocean Climate Stations as Reference Time Series and Research Aggregate Devices

The NOAA Pacific Marine Environmental Laboratory (PMEL) Ocean Climate Stations (OCS) project provides in situ measurements for quantifying air-sea interactions that couple the ocean and atmosphere. The project maintains two OceanSITES surface moorings in the North Pacific, one at the Kuroshio Extension Observatory in the Northwest Pacific subtropical recirculation gyre and the other at Station Papa in the Northeast Pacific subpolar gyre. OCS mooring time series are used as in situ references for assessing satellite and numerical weather prediction models. A spinoff of the PMEL Tropical Atmosphere Ocean (TAO) project, OCS moorings have acted as “research aggregating devices.” Working with and attracting wide-ranging partners, OCS scientists have collected process-oriented observations of variability on diurnal, synoptic, seasonal, and interannual timescales associated with anthropogenic climate change. Since 2016, they have worked to expand, test, and verify the observing capabilities of uncrewed surface vehicles and to develop observing strategies for integrating these unique, wind-powered observing platforms within the tropical Pacific and global ocean observing system. PMEL OCS has been at the center of the UN Decade of Ocean Sciences for Sustainable Development (2021–2030) effort to develop an Observing Air-Sea Interactions Strategy (OASIS) that links an expanded network of in situ air-sea interaction observations to optimized satellite observations, improved ocean and atmospheric coupling in Earth system models, and ultimately improved ocean information across an array of essential climate variables for decision-makers. This retrospective highlights not only achievements of the PMEL OCS project but also some of its challenges.

Utility of agroclimatic stations to characterize microclimate and climate change in the sherry area over the last 20 years

In this work we analyze climatic data of temperatures, rainfall, winds and bioclimatic indexes for the last 20 years obtained from 8 agroclimatic stations distributed in the cultivation areas of the Sherry. The effects of climate change have been confirmed and maximum temperatures show a clear tendency to increase, which could lead to a considerable increase in maximum temperatures in the coming years. It has been observed that the average maximum temperatures during the summer in Jerez exceed 32 ºC, while in El Puerto and Sanlúcar they are appreciably cooler and are in the order of 30.5 ºC. It has also been verified a reduction of rainfall by almost 100 L/m2 in recent years, and verifying that rainfall has a certain cyclical character. It has been tested that predominant winds in Jerez are from the east, in Sanlúcar they are from the west and El Puerto has an intermediate wind regime. Significant differences have been observed between climatic stations located at short distances, showing the convenience of having agroclimatic stations in the most important vineyard areas and in the most suitable areas of the winery districts of the cities to adequately characterize their microclimate in relation to the wine industry.